# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import print_function
from ..wrapped_decorator import signature_safe_contextmanager
from .layer_function_generator import autodoc, templatedoc
from .tensor import assign, fill_constant
from .. import core
from ..framework import Program, Variable, Operator
from ..layer_helper import LayerHelper, unique_name
from ..initializer import force_init_on_cpu
from .nn import logical_and, logical_not, logical_or
import numpy
import warnings
import six
from functools import reduce
__all__ = [
'While', 'Switch', 'increment', 'array_write', 'create_array', 'less_than',
'less_equal', 'greater_than', 'greater_equal', 'equal', 'not_equal',
'array_read', 'array_length', 'IfElse', 'DynamicRNN', 'StaticRNN',
'reorder_lod_tensor_by_rank', 'Print', 'is_empty'
]
def split_lod_tensor(input, mask, level=0):
"""
This function takes in an input that contains the complete lod information,
and takes in a mask which is used to mask certain parts of the input.
The output is the true branch and the false branch with the mask applied to
the input at a certain level in the tensor. Mainly used in IfElse to split
data into two parts.
Args:
input(tuple|list|None): The input tensor that contains complete
lod information needed to construct the output.
mask(list): A bool column vector which masks the input.
level(int): The specific lod level to split.
Returns:
tuple(Variable, Variable):
The true branch of tensor as per the mask applied to input.
The false branch of tensor as per the mask applied to input.
Examples:
.. code-block:: python
import paddle.fluid as fluid
x = fluid.layers.data(name='x', shape=[1])
x.persistable = True
y = fluid.layers.data(name='y', shape=[1])
y.persistable = True
out_true, out_false = fluid.layers.split_lod_tensor(
input=x, mask=y, level=level)
"""
helper = LayerHelper('split_lod_tensor', **locals())
out_true = helper.create_variable_for_type_inference(dtype=input.dtype)
out_false = helper.create_variable_for_type_inference(dtype=input.dtype)
helper.append_op(
type='split_lod_tensor',
inputs={
'X': input,
'Mask': mask,
},
outputs={'OutTrue': out_true,
'OutFalse': out_false},
attrs={'level': level})
return out_true, out_false
def merge_lod_tensor(in_true, in_false, x, mask, level=0):
"""
**merge_lod_tensor**
This function takes in an input :math:`x`, the True branch, the False
branch and a binary :math:`mask`. Using this information, this function
merges the True and False branches of the tensor into a single tensor as
output at a certain lod level indicated by :math:`level`. Used in IfElse
to merge the output if True block and False Block.
Args:
in_true(tuple|list|None): The True branch to be merged.
in_false(tuple|list|None): The False branch to be merged.
x(tuple|list|None): The input tensor that contains complete
lod information needed to construct the output.
mask(list): A bool column vector which masks the input.
level(int): The specific lod level to merge.
Returns:
Variable: The merged output tensor.
Examples:
.. code-block:: python
import paddle.fluid as fluid
x = layers.data(
name='x', shape=[1], dtype='float32', stop_gradient=False)
y = layers.data(
name='y', shape=[1], dtype='bool', stop_gradient=False)
level = 0
out_true, out_false = layers.split_lod_tensor(
input=x, mask=y, level=level)
out = layers.merge_lod_tensor(
in_true=out_true, in_false=out_false, mask=y, x=x, level=level)
"""
helper = LayerHelper('merge_lod_tensor', **locals())
out = helper.create_variable_for_type_inference(dtype=in_true.dtype)
helper.append_op(
type='merge_lod_tensor',
inputs={'X': x,
'Mask': mask,
'InTrue': in_true,
'InFalse': in_false},
outputs={'Out': out},
attrs={'level': level})
return out
def Print(input,
first_n=-1,
message=None,
summarize=20,
print_tensor_name=True,
print_tensor_type=True,
print_tensor_shape=True,
print_tensor_lod=True,
print_phase='both'):
'''
**Print operator**
This creates a print op that will print when a tensor is accessed.
Wraps the tensor passed in so that whenever that a tensor is accessed,
the message `message` is printed, along with the current value of the
tensor `t`.
Args:
input (Variable): A Tensor to print.
summarize (int): Number of elements in the tensor to be print. If it's
vaule is -1, then all elements in the tensor will be print.
message (str): A string message to print as a prefix.
first_n (int): Only log `first_n` number of times.
print_tensor_name (bool, optional): Print the tensor name. Default: True.
print_tensor_type (bool, optional): Print the tensor type. Defaultt: True.
print_tensor_shape (bool, optional): Print the tensor shape. Default: True.
print_tensor_lod (bool, optional): Print the tensor lod. Default: True.
print_phase (str): Which phase to displace, including 'forward',
'backward' and 'both'. Default: 'both'. If set to 'backward', will
only print the gradients of input tensor; If set to 'both', will
both print the input tensor itself and the gradients of input tensor.
Returns:
Variable: Output tensor.
NOTES:
The input and output are two different variables, and in the
following process, you should use the output variable but not the input,
otherwise, the print layer doesn't have backward.
Examples:
.. code-block:: python
import paddle.fluid as fluid
input = fluid.layers.fill_constant(shape=[10,2], value=3, dtype='int64')
input = fluid.layers.Print(input, message="The content of input layer:")
main_program = fluid.default_main_program()
exe = fluid.Executor(fluid.CPUPlace())
exe.run(main_program)
Output at runtime:
.. code-block:: bash
The content of input layer: The place is:CPUPlace
Tensor[fill_constant_0.tmp_0]
shape: [10,2,]
dtype: x
data: 3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,
'''
helper = LayerHelper('print' + "_" + input.name, **locals())
output = helper.create_variable_for_type_inference(input.dtype)
helper.append_op(
type='print',
inputs={'In': input},
outputs={'Out': output},
attrs={
'first_n': first_n,
'summarize': summarize,
'message': message or "",
'print_tensor_name': print_tensor_name,
'print_tensor_type': print_tensor_type,
'print_tensor_shape': print_tensor_shape,
'print_tensor_lod': print_tensor_lod,
'print_phase': print_phase.upper()
})
return output
class BlockGuard(object):
"""
BlockGuard class.
BlockGuard class is used to create a sub-block in a program by
using the Python `with` keyword.
"""
def __init__(self, main_program):
if not isinstance(main_program, Program):
raise TypeError("BlockGuard takes a program")
self.main_program = main_program
def __enter__(self):
self.main_program._create_block()
def __exit__(self, exc_type, exc_val, exc_tb):
self.main_program._rollback()
if exc_type is not None:
return False # re-raise exception
return True
class BlockGuardWithCompletion(BlockGuard):
"""
BlockGuardWithCompletion class.
BlockGuardWithCompletion class is used to create an op with a block in a program.
"""
def __init__(self, rnn):
if not isinstance(rnn, StaticRNN):
raise TypeError("BlockGuardWithCompletion takes a StaticRNN")
super(BlockGuardWithCompletion, self).__init__(rnn.helper.main_program)
self.rnn = rnn
def __enter__(self):
self.rnn.status = StaticRNN.IN_RNN_BLOCK
return super(BlockGuardWithCompletion, self).__enter__()
def __exit__(self, exc_type, exc_val, exc_tb):
if exc_type is not None:
return False
self.rnn.status = StaticRNN.AFTER_RNN_BLOCK
self.rnn._complete_op()
return super(BlockGuardWithCompletion, self).__exit__(exc_type, exc_val,
exc_tb)
class StaticRNNMemoryLink(object):
"""
StaticRNNMemoryLink class.
StaticRNNMemoryLink class is used to create a link between two
memory cells of a StaticRNN.
NOTE: This is a internal data structure of a very low-level API.
Please use StaticRNN instead.
Args:
init(Variable): the initial variable for Memory.
pre_mem(Variable): the memory variable in previous time step.
mem(Variable): the memory variable in current time step.
"""
def __init__(self, init, pre_mem, mem=None):
self.init = init
self.pre_mem = pre_mem
self.mem = mem
class StaticRNN(object):
"""
StaticRNN class.
The StaticRNN can process a batch of sequence data. The first dimension of inputs
represents sequence length, the length of each input sequence must be equal.
StaticRNN will unfold sequence into time steps, user needs to define how to process
each time step during the :code:`with` step.
Args:
name (str, optional): Please refer to :ref:`api_guide_Name`, Default None.
Examples:
.. code-block:: python
import paddle.fluid as fluid
import paddle.fluid.layers as layers
vocab_size, hidden_size=10000, 200
x = fluid.data(name="x", shape=[None, 1, 1], dtype='int64')
# create word sequence
x_emb = layers.embedding(
input=x,
size=[vocab_size, hidden_size],
dtype='float32',
is_sparse=False)
# transform batch size to dim 1
x_emb = layers.transpose(x_emb, perm=[1, 0, 2])
rnn = fluid.layers.StaticRNN()
with rnn.step():
# mark created x_emb as input, each step process a word
word = rnn.step_input(x_emb)
# create prev memory parameter, batch size comes from word
prev = rnn.memory(shape=[-1, hidden_size], batch_ref = word)
hidden = fluid.layers.fc(input=[word, prev], size=hidden_size, act='relu')
# use hidden to update prev
rnn.update_memory(prev, hidden)
# mark hidden as output
rnn.step_output(hidden)
# get StaticrNN final output
result = rnn()
"""
BEFORE_RNN_BLOCK = 0
IN_RNN_BLOCK = 1
AFTER_RNN_BLOCK = 2
def __init__(self, name=None):
self.helper = LayerHelper("static_rnn", name=name)
self.memories = {} # memory map, from pre_mem.name --> MemoryLink
self.inputs = [] # input variable list in current block
self.outputs = [] # output variable list in parent block
self.status = StaticRNN.BEFORE_RNN_BLOCK # status flag.
# sequence length, since it is a static RNN, sequence length are fixed.
self.seq_len = None
def step(self):
"""
Define operators in each step. step is used in :code:`with` block, OP in :code:`with` block
will be executed sequence_len times (sequence_len is the length of input)
"""
return BlockGuardWithCompletion(self)
def _assert_in_rnn_block_(self, method):
if self.status != StaticRNN.IN_RNN_BLOCK:
raise ValueError("You must invoke {0} in rnn block".format(method))
def memory(self,
init=None,
shape=None,
batch_ref=None,
init_value=0.0,
init_batch_dim_idx=0,
ref_batch_dim_idx=1):
"""
Create a memory variable for static rnn.
If the :code:`init` is not None, :code:`memory` will be initialized by
this Variable. If the :code:`init` is None, :code:`shape` and :code:`batch_ref`
must be set, and this function will create a new variable with shape and batch_ref
to initialize :code:`init` Variable.
Args:
init(Variable, optional): Tensor used to init memory. If it is not set,
:code:`shape` and :code:`batch_ref` must be provided.
Default: None.
shape(list|tuple): When :code:`init` is None use this arg to initialize memory shape.
NOTE the shape does not contain batch_size. Default: None.
batch_ref(Variable, optional): When :code:`init` is None, memory's batch size will
be set as batch_ref's ref_batch_dim_idx value. Default: None.
init_value(float, optional): When :code:`init` is None, used to init memory's value. Default: 0.0.
init_batch_dim_idx(int, optional): the batch_size axis of the :code:`init` Variable. Default: 0.
ref_batch_dim_idx(int, optional): the batch_size axis of the :code:`batch_ref` Variable. Default: 1.
Returns:
Variable: The memory variable.
Examples 1:
.. code-block:: python
import paddle.fluid as fluid
import paddle.fluid.layers as layers
vocab_size, hidden_size=10000, 200
x = fluid.data(name="x", shape=[None, 1, 1], dtype='int64')
# create word sequence
x_emb = layers.embedding(
input=x,
size=[vocab_size, hidden_size],
dtype='float32',
is_sparse=False)
# transform batch size to dim 1
x_emb = layers.transpose(x_emb, perm=[1, 0, 2])
rnn = fluid.layers.StaticRNN()
with rnn.step():
# mark created x_emb as input, each step process a word
word = rnn.step_input(x_emb)
# create prev memory parameter, batch size comes from word
prev = rnn.memory(shape=[-1, hidden_size], batch_ref = word)
hidden = fluid.layers.fc(input=[word, prev], size=hidden_size, act='relu')
# use hidden to update prev
rnn.update_memory(prev, hidden)
Examples 2:
.. code-block:: python
import paddle.fluid as fluid
import paddle.fluid.layers as layers
vocab_size, hidden_size=10000, 200
x = fluid.data(name="x", shape=[None, 1, 1], dtype='int64')
# create word sequence
x_emb = layers.embedding(
input=x,
size=[vocab_size, hidden_size],
dtype='float32',
is_sparse=False)
# transform batch size to dim 1
x_emb = layers.transpose(x_emb, perm=[1, 0, 2])
boot_memory = fluid.layers.data(name='boot', shape=[hidden_size], dtype='float32', lod_level=1)
rnn = fluid.layers.StaticRNN()
with rnn.step():
# mark created x_emb as input, each step process a word
word = rnn.step_input(x_emb)
# init memory
prev = rnn.memory(init=boot_memory)
hidden = fluid.layers.fc(input=[word, prev], size=hidden_size, act='relu')
# update hidden with prev
rnn.update_memory(prev, hidden)
"""
self._assert_in_rnn_block_('memory')
if init is None:
if shape is None or batch_ref is None:
raise ValueError(
"if init is None, memory at least need shape and batch_ref")
parent_block = self._parent_block()
var_name = unique_name.generate_with_ignorable_key("@".join(
[self.helper.name, "memory_boot"]))
boot_var = parent_block.create_var(
name=var_name,
shape=shape,
dtype=batch_ref.dtype,
persistable=False)
parent_block.append_op(
type="fill_constant_batch_size_like",
inputs={'Input': [batch_ref]},
outputs={'Out': [boot_var]},
attrs={
'value': init_value,
'shape': boot_var.shape,
'dtype': boot_var.dtype,
'input_dim_idx': ref_batch_dim_idx,
'output_dim_idx': init_batch_dim_idx
})
return self.memory(init=boot_var)
else:
pre_mem = self.helper.create_variable(
name=unique_name.generate_with_ignorable_key("@".join(
[self.helper.name, "mem"])),
dtype=init.dtype,
shape=init.shape)
self.memories[pre_mem.name] = StaticRNNMemoryLink(
init=init, pre_mem=pre_mem)
return pre_mem
def step_input(self, x):
"""
Mark a sequence as a StaticRNN input.
Args:
x(Variable): The input sequence, the shape of x
should be [seq_len, ...].
Returns:
Variable: The current time step data in the input sequence.
Examples:
.. code-block:: python
import paddle.fluid as fluid
import paddle.fluid.layers as layers
vocab_size, hidden_size=10000, 200
x = fluid.data(name="x", shape=[None, 1, 1], dtype='int64')
# create word sequence
x_emb = layers.embedding(
input=x,
size=[vocab_size, hidden_size],
dtype='float32',
is_sparse=False)
# transform batch size to dim 1
x_emb = layers.transpose(x_emb, perm=[1, 0, 2])
rnn = fluid.layers.StaticRNN()
with rnn.step():
# mark created x_emb as input, each step process a word
word = rnn.step_input(x_emb)
# create prev memory parameter, batch size comes from word
prev = rnn.memory(shape=[-1, hidden_size], batch_ref = word)
hidden = fluid.layers.fc(input=[word, prev], size=hidden_size, act='relu')
# use hidden to update prev
rnn.update_memory(prev, hidden)
"""
self._assert_in_rnn_block_('step_input')
if not isinstance(x, Variable):
raise TypeError("step input takes a Variable")
if self.seq_len is None:
self.seq_len = x.shape[0]
elif x.shape[0] != -1 and self.seq_len != x.shape[0]:
raise ValueError("Static RNN only take fix seq_len input")
ipt = self.helper.create_variable(
name=x.name, dtype=x.dtype, shape=list(x.shape[1:]), type=x.type)
self.inputs.append(ipt)
return ipt
def step_output(self, o):
"""
Mark a sequence as a StaticRNN output.
Args:
o(Variable): The output sequence.
Returns:
None.
Examples:
.. code-block:: python
import paddle.fluid as fluid
import paddle.fluid.layers as layers
vocab_size, hidden_size=10000, 200
x = fluid.data(name="x", shape=[None, 1, 1], dtype='int64')
# create word sequence
x_emb = layers.embedding(
input=x,
size=[vocab_size, hidden_size],
dtype='float32',
is_sparse=False)
# transform batch size to dim 1
x_emb = layers.transpose(x_emb, perm=[1, 0, 2])
rnn = fluid.layers.StaticRNN()
with rnn.step():
# mark created x_emb as input, each step process a word
word = rnn.step_input(x_emb)
# create prev memory parameter, batch size comes from word
prev = rnn.memory(shape=[-1, hidden_size], batch_ref = word)
hidden = fluid.layers.fc(input=[word, prev], size=hidden_size, act='relu')
# use hidden to update prev
rnn.update_memory(prev, hidden)
rnn.step_output(hidden)
result = rnn()
"""
self._assert_in_rnn_block_('step_output')
if not isinstance(o, Variable):
raise TypeError("step output takes a Variable")
tmp_o = self.helper.create_variable_for_type_inference(dtype=o.dtype)
self.helper.append_op(
type='rnn_memory_helper',
inputs={'X': [o]},
outputs={'Out': tmp_o},
attrs={'dtype': o.dtype})
out_var = self._parent_block().create_var(
name=tmp_o.name,
shape=[self.seq_len] + list(tmp_o.shape),
dtype=tmp_o.dtype)
self.outputs.append(out_var)
def output(self, *outputs):
"""
Mark the StaticRNN output variables.
Args:
outputs: The output Tensor, can mark multiple variables as output
Returns:
None
Examples:
.. code-block:: python
import paddle.fluid as fluid
import paddle.fluid.layers as layers
vocab_size, hidden_size=10000, 200
x = fluid.data(name="x", shape=[None, 1, 1], dtype='int64')
# create word sequence
x_emb = layers.embedding(
input=x,
size=[vocab_size, hidden_size],
dtype='float32',
is_sparse=False)
# transform batch size to dim 1
x_emb = layers.transpose(x_emb, perm=[1, 0, 2])
rnn = fluid.layers.StaticRNN()
with rnn.step():
# mark created x_emb as input, each step process a word
word = rnn.step_input(x_emb)
# create prev memory parameter, batch size comes from word
prev = rnn.memory(shape=[-1, hidden_size], batch_ref = word)
hidden = fluid.layers.fc(input=[word, prev], size=hidden_size, act='relu')
# use hidden to update prev
rnn.update_memory(prev, hidden)
# mark each step's hidden and word as output
rnn.output(hidden, word)
result = rnn()
"""
for each in outputs:
self.step_output(each)
def update_memory(self, mem, var):
"""
Update the memory from :code:`mem` to :code:`var`.
Args:
mem(Variable): the memory variable.
var(Variable): the plain variable generated in RNN block, used to update memory.
var and mem should hava same dims and data type.
Returns:
None
"""
if not isinstance(mem, Variable) or not isinstance(var, Variable):
raise TypeError("update memory should take variables")
self.memories[mem.name].mem = var
def _parent_block(self):
prog = self.helper.main_program
parent_idx = prog.current_block().parent_idx
assert parent_idx >= 0
parent_block = prog.block(parent_idx)
return parent_block
def __call__(self, *args, **kwargs):
if self.status != StaticRNN.AFTER_RNN_BLOCK:
raise ValueError("RNN output can only be retrieved after rnn block")
if len(self.outputs) == 0:
raise ValueError("RNN has no output")
elif len(self.outputs) == 1:
return self.outputs[0]
else:
return self.outputs
def _complete_op(self):
main_program = self.helper.main_program
rnn_block = main_program.current_block()
parent_block = self._parent_block()
local_inputs = set()
for op in rnn_block.ops:
assert isinstance(op, Operator)
for oname in op.output_names:
for out_var_name in op.output(oname):
local_inputs.add(out_var_name)
for var in self.inputs:
local_inputs.add(var.name)
for m in self.memories:
local_inputs.add(m)
# NOTE(zcd): the params have two categories of variables.
# - the variables that are the out of StaticRnn.
# - the variables that are the parameters of some layers, for example, conv2d.
params = list()
for op in rnn_block.ops:
assert isinstance(op, Operator)
for iname in op.input_names:
for in_var_name in op.input(iname):
if in_var_name not in local_inputs:
params.append(in_var_name)
parameters = [parent_block.var(name) for name in set(params)]
step_scope = parent_block.create_var(
type=core.VarDesc.VarType.STEP_SCOPES)
inlinks = [parent_block.var(i.name) for i in self.inputs]
outlinks = self.outputs
# NOTE(zcd): the states maybe empty in some case.
boot_memories = []
pre_memories = []
memories = []
for _, mem in six.iteritems(self.memories):
boot_memories.append(mem.init)
pre_memories.append(mem.pre_mem.name)
assert mem.mem is not None, "%s should be updated in every step." % (
mem.init.name)
mem_var = rnn_block.var(mem.mem.name)
assert isinstance(mem_var, Variable)
new_mem = self.helper.create_variable_for_type_inference(
dtype=mem_var.dtype)
rnn_block.append_op(
type='rnn_memory_helper',
inputs={'X': [mem_var]},
outputs={'Out': [new_mem]},
attrs={'dtype': mem_var.dtype})
memories.append(new_mem.name)
parent_block.append_op(
type='recurrent',
inputs={
'inputs': inlinks,
'initial_states': boot_memories,
'parameters': parameters
},
outputs={'outputs': outlinks,
'step_scopes': [step_scope]},
attrs={
'has_states': len(pre_memories) > 0,
'ex_states': pre_memories,
'states': memories,
'sub_block': rnn_block
})
class WhileGuard(BlockGuard):
def __init__(self, while_op):
if not isinstance(while_op, While):
raise TypeError("WhileGuard takes a while op")
super(WhileGuard, self).__init__(while_op.helper.main_program)
self.while_op = while_op
def __enter__(self):
self.while_op.status = While.IN_WHILE_BLOCK
return super(WhileGuard, self).__enter__()
def __exit__(self, exc_type, exc_val, exc_tb):
if exc_type is not None:
return False
self.while_op.status = While.AFTER_WHILE_BLOCK
self.while_op._complete()
return super(WhileGuard, self).__exit__(exc_type, exc_val, exc_tb)
class While(object):
"""
while loop control flow. Repeat while body until cond is False.
Args:
cond(Variable): A Tensor whose data type is bool controlling whether to continue looping.
is_test(bool, optional): A flag indicating whether execution is in test phase. Default value is None.
name(str, optional): The default value is None. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name` .
Examples:
.. code-block:: python
import paddle.fluid as fluid
import numpy as np
i = fluid.layers.fill_constant(shape=[1], dtype='int64', value=0) # loop counter
loop_len = fluid.layers.fill_constant(shape=[1],dtype='int64', value=10) # loop length
cond = fluid.layers.less_than(x=i, y=loop_len)
while_op = fluid.layers.While(cond=cond)
with while_op.block():
i = fluid.layers.increment(x=i, value=1, in_place=True)
fluid.layers.less_than(x=i, y=loop_len, cond=cond)
exe = fluid.Executor(fluid.CPUPlace())
exe.run(fluid.default_startup_program())
res = exe.run(fluid.default_main_program(), feed={}, fetch_list=[i])
print(res) # [array([10])]
"""
BEFORE_WHILE_BLOCK = 0
IN_WHILE_BLOCK = 1
AFTER_WHILE_BLOCK = 2
def __init__(self, cond, is_test=False, name=None):
self.helper = LayerHelper("while", name=name)
self.status = While.BEFORE_WHILE_BLOCK
if not isinstance(cond, Variable):
raise TypeError("condition should be a variable")
assert isinstance(cond, Variable)
if cond.dtype != core.VarDesc.VarType.BOOL:
raise TypeError("condition should be a boolean variable")
if reduce(lambda a, b: a * b, cond.shape, 1) != 1:
raise TypeError(
"condition expected shape as [], but given shape as {0}.".
format(list(cond.shape)))
self.cond_var = cond
self.is_test = is_test
def block(self):
return WhileGuard(self)
def _complete(self):
main_program = self.helper.main_program
while_block = main_program.current_block()
parent_block = main_program.block(main_program.current_block()
.parent_idx)
inner_outputs = {self.cond_var.name}
x_name_list = set()
for op in while_block.ops:
for iname in op.input_names:
for in_var_name in op.input(iname):
if in_var_name not in inner_outputs:
x_name_list.add(in_var_name)
for oname in op.output_names:
for out_var_name in op.output(oname):
inner_outputs.add(out_var_name)
out_vars = []
for inner_out_name in inner_outputs:
inner_var = parent_block._find_var_recursive(inner_out_name)
if inner_var:
out_vars.append(inner_var)
step_scope = parent_block.create_var(
type=core.VarDesc.VarType.STEP_SCOPES)
parent_block.append_op(
type='while',
inputs={
'X': [
parent_block._var_recursive(x_name)
for x_name in x_name_list
],
'Condition': [self.cond_var]
},
outputs={'Out': out_vars,
'StepScopes': [step_scope]},
attrs={'sub_block': while_block,
"is_test": self.is_test})
def lod_rank_table(x, level=0):
"""
LoD Rank Table Operator. Given an input variable **x** and a level number
of LoD, this layer creates a LodRankTable object. A LoDRankTable object
contains a list of bi-element tuples. Each tuple consists of an index and
a length, both of which are int type. Refering to specified level of LoD,
the index is the sequence index number and the length representes the
sequence length. Please note that the list is ranked in descending order by
the length. The following is an example:
.. code-block:: text
x is a LoDTensor:
x.lod = [[2, 1],
[5, 1, 1]]
x.data = [a, b, c, d, e, f, g]
1. set level to 0:
Create lod rank table:
lod_rank_table_obj = lod_rank_table(x, level=0)
Get:
lod_rank_table_obj.items() = [(0, 2), (1, 1)]
2. set level to 1:
Create lod rank table:
lod_rank_table_obj = lod_rank_table(x, level=1)
Get:
lod_rank_table_obj.items() = [(0, 5), (1, 1), (2, 1)]
Args:
x (Variable): Input variable, a LoDTensor based which to create the lod
rank table.
level (int): Specify the LoD level, on which to create the lod rank
table.
Returns:
Variable: The created LoDRankTable object.
Examples:
.. code-block:: python
import paddle.fluid as fluid
x = fluid.layers.data(name='x', shape=[10],
dtype='float32', lod_level=1)
out = layers.lod_rank_table(x=x, level=0)
"""
helper = LayerHelper("lod_rank_table", **locals())
table = helper.create_variable(
type=core.VarDesc.VarType.LOD_RANK_TABLE,
name=unique_name.generate("lod_rank_table"))
helper.append_op(
type='lod_rank_table',
inputs={'X': x},
outputs={'Out': table},
attrs={'level': level})
return table
@templatedoc()
def max_sequence_len(rank_table):
"""
${comment}
>>> import paddle.fluid as fluid
>>> x = fluid.layers.data(name='x', shape=[10], dtype='float32',
>>> lod_level=1)
>>> rank_table = layers.lod_rank_table(x=x, level=0)
>>> max_seq_len = layers.max_sequence_len(rank_table)
Args:
rank_table(${rank_table_type}): ${rank_table_comment}.
Returns:
${out_comment}.
"""
helper = LayerHelper("max_seqence_len", **locals())
res = helper.create_variable_for_type_inference(dtype="int64")
helper.append_op(
type="max_sequence_len",
inputs={"RankTable": rank_table},
outputs={"Out": res})
return res
def lod_tensor_to_array(x, table):
"""
Convert a LoDTensor to a LoDTensorArray.
This function split a LoDTesnor to a LoDTensorArray according to its LoD
information. LoDTensorArray is an alias of C++ std::vector<LoDTensor> in
PaddlePaddle. The generated LoDTensorArray of this function can be further read
or written by `read_from_array()` and `write_to_array()` operators. However,
this function is generally an internal component of PaddlePaddle `DynamicRNN`.
Users should not use it directly.
Args:
x (Variable|list): The LoDTensor to be converted to a LoDTensorArray.
table (ParamAttr|list): The variable that stores the level of lod
which is ordered by sequence length in
descending order. It is generally generated
by `layers.lod_rank_table()` API.
Returns:
Variable: The LoDTensorArray that has been converted from the input tensor.
Examples:
.. code-block:: python
import paddle.fluid as fluid
x = fluid.layers.data(name='x', shape=[10])
table = fluid.layers.lod_rank_table(x, level=0)
array = fluid.layers.lod_tensor_to_array(x, table)
"""
helper = LayerHelper("lod_tensor_to_array", **locals())
array = helper.create_variable(
name=unique_name.generate("lod_tensor_to_array"),
type=core.VarDesc.VarType.LOD_TENSOR_ARRAY,
dtype=x.dtype)
helper.append_op(
type='lod_tensor_to_array',
inputs={'X': x,
'RankTable': table},
outputs={'Out': array})
return array
def array_to_lod_tensor(x, table):
"""Convert a LoD_Tensor_Aarry to an LoDTensor.
Args:
x (Variable|list): The lod tensor array to be converted to a tensor.
table (ParamAttr|list): The variable that stores the level of lod
which is ordered by sequence length in
descending order.
Returns:
Variable: The variable of type tensor that has been converted
from an array.
Examples:
.. code-block:: python
import paddle.fluid as fluid
x = fluid.layers.data(name='x', shape=[10])
table = fluid.layers.lod_rank_table(x, level=0)
array = fluid.layers.lod_tensor_to_array(x, table)
lod_tensor = fluid.layers.array_to_lod_tensor(array, table)
"""
helper = LayerHelper("array_to_lod_tensor", **locals())
tmp = helper.create_variable_for_type_inference(dtype=x.dtype)
helper.append_op(
type="array_to_lod_tensor",
inputs={'X': x,
'RankTable': table},
outputs={'Out': tmp})
return tmp
def increment(x, value=1.0, in_place=True):
"""
The OP is usually used for control flow to increment the data of :attr:`x` by an amount :attr:`value`.
Notice that the number of elements in :attr:`x` must be equal to 1.
Parameters:
x (Variable): A tensor that must alway contain only one element, its data type supports
float32, float64, int32 and int64.
value (float, optional): The amount to increment the data of :attr:`x`. Default: 1.0.
in_place (bool, optional): Whether the OP should be performed in-place. Default: True.
Returns:
Variable: The elementwise-incremented tensor with the same shape and data type as :attr:`x`.
Examples:
.. code-block:: python
import paddle.fluid as fluid
counter = fluid.layers.zeros(shape=[1], dtype='float32') # [0.]
fluid.layers.increment(counter) # [1.]
"""
helper = LayerHelper("increment", **locals())
if not in_place:
out = helper.create_variable_for_type_inference(dtype=x.dtype)
else:
out = x
helper.append_op(
type='increment',
inputs={'X': [x]},
outputs={'Out': [out]},
attrs={'step': float(value)})
return out
def array_write(x, i, array=None):
"""
This OP writes the input ``x`` into the i-th position of the ``array``
:ref:`api_fluid_LoDTensorArray` and returns the modified array.
If ``array`` is none, a new LoDTensorArray will be created and returned.
This OP is often used together with :ref:`api_fluid_layers_array_read` OP.
Args:
x (Variable): The input data to be written into array. It's multi-dimensional
Tensor or LoDTensor. Data type: float32, float64, int32, int64.
i (Variable): 1-D Tensor with shape [1], which represents the position into which
``x`` is written. Data type: int64.
array (LoDTensorArray, optional): The LoDTensorArray into which ``x`` is written.
The default value is None, when a new LoDTensorArray will be created and returned
as a result.
Returns:
Variable: The input ``array`` after ``x`` is written into.
Examples:
.. code-block:: python
import paddle.fluid as fluid
tmp = fluid.layers.fill_constant(shape=[3, 2], dtype='int64', value=5)
i = fluid.layers.fill_constant(shape=[1], dtype='int64', value=10)
# Write tmp into the position of arr with subscript 10 and return arr.
arr = fluid.layers.array_write(tmp, i=i)
# Now, arr is a LoDTensorArray with length 11. We can use array_read OP to read
# the data at subscript 10 and print it out.
item = fluid.layers.array_read(arr, i=i)
input = fluid.layers.Print(item, message="The content of i-th LoDTensor:")
main_program = fluid.default_main_program()
exe = fluid.Executor(fluid.CPUPlace())
exe.run(main_program)
# The printed result is:
# 1570533133 The content of i-th LoDTensor: The place is:CPUPlace
# Tensor[array_read_0.tmp_0]
# shape: [3,2,]
# dtype: l
# data: 5,5,5,5,5,5,
# the output is 2-D Tensor with shape [3,2], which is tmp above.
# dtype is the corresponding C++ data type, which may vary in different environments.
# Eg: if the data type of tensor is int64, then the corresponding C++ data type is int64_t,
# so the dtype value is typeid(int64_t).Name(), which is 'x' on MacOS, 'l' on Linux,
# and '__int64' on Windows. They both represent 64-bit integer variables.
"""
helper = LayerHelper('array_write', **locals())
if array is None:
array = helper.create_variable(
name="{0}.out".format(helper.name),
type=core.VarDesc.VarType.LOD_TENSOR_ARRAY,
dtype=x.dtype)
helper.append_op(
type='write_to_array',
inputs={'X': [x],
'I': [i]},
outputs={'Out': [array]})
return array
def create_array(dtype):
"""
This OP creates an LOD_TENSOR_ARRAY. It is used as
the input of :ref:`api_fluid_layers_array_read` and
:ref:`api_fluid_layers_array_write`. Also it can be used
with :ref:`api_fluid_layers_While` to create RNN network.
Args:
dtype (str): The data type of the elements in the lod_tensor_array.
Support data type: float32, float64, int32, int64.
Returns:
Variable: The empty lod_tensor_array. The data type of elements in Tensor is ``dtype``.
Examples:
.. code-block:: python
import paddle.fluid as fluid
data = fluid.layers.create_array(dtype='float32') # Create a float32 LoDTensorArray.
"""
helper = LayerHelper("array", **locals())
return helper.create_variable(
name="{0}.out".format(helper.name),
type=core.VarDesc.VarType.LOD_TENSOR_ARRAY,
dtype=dtype)
@templatedoc()
def less_than(x, y, force_cpu=None, cond=None):
"""
${comment}
Args:
x(${x_type}): ${x_comment}.
y(${y_type}): ${y_comment}.
force_cpu(${force_cpu_type}): ${force_cpu_comment}.
cond(Variable|None): Optional output variable to store the result of *less_than*
Returns:
${out_comment}.
Examples:
.. code-block:: python
import paddle.fluid as fluid
import numpy as np
# Graph Organizing
x = fluid.layers.data(name='x', shape=[2], dtype='float64')
y = fluid.layers.data(name='y', shape=[2], dtype='float64')
result = fluid.layers.less_than(x=x, y=y)
# The comment lists another available method.
# result = fluid.layers.fill_constant(shape=[2], dtype='float64', value=0)
# fluid.layers.less_than(x=x, y=y, cond=result)
# Create an executor using CPU as example
exe = fluid.Executor(fluid.CPUPlace())
# Execute
x_i = np.array([[1, 2], [3, 4]]).astype(np.float64)
y_i = np.array([[2, 2], [1, 3]]).astype(np.float64)
result_value, = exe.run(fluid.default_main_program(), feed={'x':x_i, 'y':y_i}, fetch_list=[result])
print(result_value) # [[True, False], [False, False]]
"""
helper = LayerHelper("less_than", **locals())
if cond is None:
cond = helper.create_variable_for_type_inference(dtype='bool')
cond.stop_gradient = True
attrs = dict()
if force_cpu is not None:
attrs['force_cpu'] = force_cpu
elif force_init_on_cpu():
attrs['force_cpu'] = force_init_on_cpu()
helper.append_op(
type='less_than',
inputs={'X': [x],
'Y': [y]},
outputs={'Out': [cond]},
attrs=attrs)
return cond
@templatedoc()
def less_equal(x, y, cond=None):
"""
This OP returns the truth value of :math:`x <= y` elementwise, which is equivalent function to the overloaded operator `<=`.
Args:
x(Variable): First input to compare which is N-D tensor. The input data type should be float32, float64, int32, int64.
y(Variable): Second input to compare which is N-D tensor. The input data type should be float32, float64, int32, int64.
cond(Variable, optional): If is :attr:`None`, the op will create a variable as output tensor, the input shape and data type of \
this tensor is the same as input :attr:`x`. If is not :attr:`None`, the op will set the variable as output tensor, the input shape \
and data type of this tensor should be the same as input :attr:`x`. Default value is :attr:`None`.
Returns:
Variable, the output data type is bool.: The tensor variable storing the output, the output shape is the same as input :attr:`x`.
Examples:
.. code-block:: python
import paddle.fluid as fluid
import numpy as np
label = fluid.layers.assign(np.array([1, 3], dtype='int32'))
limit = fluid.layers.assign(np.array([1, 2], dtype='int32'))
out = fluid.layers.less_equal(x=label, y=limit) #out=[True, False]
out1 = label<= limit #out1=[True, False]
"""
helper = LayerHelper("less_equal", **locals())
if cond is None:
cond = helper.create_variable_for_type_inference(dtype='bool')
cond.stop_gradient = True
attrs = dict()
if force_init_on_cpu():
attrs['force_cpu'] = force_init_on_cpu()
helper.append_op(
type='less_equal',
inputs={'X': [x],
'Y': [y]},
outputs={'Out': [cond]},
attrs=attrs)
return cond
@templatedoc()
def greater_than(x, y, cond=None):
"""
This OP returns the truth value of :math:`x > y` elementwise, which is equivalent function to the overloaded operator `>`.
Args:
x(Variable): First input to compare which is N-D tensor. The input data type should be float32, float64, int32, int64.
y(Variable): Second input to compare which is N-D tensor. The input data type should be float32, float64, int32, int64.
cond(Variable, optional): If is :attr:`None`, the op will create a variable as output tensor, the shape and data type of this \
tensor is the same as input :attr:`x` . If is not :attr:`None`, the op will set the variable as output tensor, the shape and data type \
of this tensor should be the same as input :attr:`x` . Default value is :attr:`None`.
Returns:
Variable, the output data type is bool.: The tensor variable storing the output, the output shape is the same as input :attr:`x` .
Examples:
.. code-block:: python
import paddle.fluid as fluid
import numpy as np
label = fluid.layers.assign(np.array([2, 3], dtype='int32'))
limit = fluid.layers.assign(np.array([3, 2], dtype='int32'))
out = fluid.layers.greater_than(x=label, y=limit) #out=[False, True]
out1 = label > limit #out1=[False, True]
"""
helper = LayerHelper("greater_than", **locals())
if cond is None:
cond = helper.create_variable_for_type_inference(dtype='bool')
cond.stop_gradient = True
attrs = dict()
if force_init_on_cpu():
attrs['force_cpu'] = force_init_on_cpu()
helper.append_op(
type='greater_than',
inputs={'X': [x],
'Y': [y]},
outputs={'Out': [cond]},
attrs=attrs)
return cond
@templatedoc()
def greater_equal(x, y, cond=None):
"""
This OP returns the truth value of :math:`x >= y` elementwise, which is equivalent function to the overloaded operator `>=`.
Args:
x(Variable): First input to compare which is N-D tensor. The input data type should be float32, float64, int32, int64.
y(Variable): Second input to compare which is N-D tensor. The input data type should be float32, float64, int32, int64.
cond(Variable, optional): If is :attr:`None` , the op will create a variable as output tensor, the shape and data type of this \
tensor is the same as input :attr:`x`. If is not :attr:`None` , the op will set the variable as output tensor, the shape and data \
type of this tensor is the same as input :attr:`x`. Default value is :attr:`None`.
Returns:
Variable, the output data type is bool.: The tensor variable storing the output, the output shape is the same as input :attr:`x`.
Examples:
.. code-block:: python
import paddle.fluid as fluid
import numpy as np
label = fluid.layers.assign(np.array([2, 2], dtype='int32'))
limit = fluid.layers.assign(np.array([2, 3], dtype='int32'))
out = fluid.layers.greater_equal(x=label, y=limit) #out=[True, False]
out_1 = label >= limit #out1=[True, False]
"""
helper = LayerHelper("greater_equal", **locals())
if cond is None:
cond = helper.create_variable_for_type_inference(dtype='bool')
cond.stop_gradient = True
attrs = dict()
if force_init_on_cpu():
attrs['force_cpu'] = force_init_on_cpu()
helper.append_op(
type='greater_equal',
inputs={'X': [x],
'Y': [y]},
outputs={'Out': [cond]},
attrs=attrs)
return cond
def equal(x, y, cond=None):
"""
This layer returns the truth value of :math:`x == y` elementwise.
Args:
x(Variable): Tensor, data type is float32, float64, int32, int64.
y(Variable): Tensor, data type is float32, float64, int32, int64.
cond(Variable, optional): Optional output which can be any created
Variable that meets the requirements to store the result of *equal*.
if cond is None, a new Varibale will be created to store the result.
Returns:
Variable: output Tensor, it's shape is the same as the input's Tensor,
and the data type is bool.
Examples:
.. code-block:: python
import paddle.fluid as fluid
import numpy as np
out_cond =fluid.data(name="input1", shape=[2], dtype='bool')
label = fluid.layers.assign(np.array([3, 3], dtype="int32"))
limit = fluid.layers.assign(np.array([3, 2], dtype="int32"))
label_cond = fluid.layers.assign(np.array([1, 2], dtype="int32"))
out1 = fluid.layers.equal(x=label,y=limit) #out1=[True, False]
out2 = fluid.layers.equal(x=label_cond,y=limit, cond=out_cond) #out2=[False, True] out_cond=[False, True]
"""
helper = LayerHelper("equal", **locals())
if cond is None:
cond = helper.create_variable_for_type_inference(dtype='bool')
cond.stop_gradient = True
helper.append_op(
type='equal', inputs={'X': [x],
'Y': [y]}, outputs={'Out': [cond]})
return cond
def not_equal(x, y, cond=None):
"""
This OP returns the truth value of :math:`x != y` elementwise, which is equivalent function to the overloaded operator `!=`.
Args:
x(Variable): First input to compare which is N-D tensor. The input data type should be float32, float64, int32, int64.
y(Variable): Second input to compare which is N-D tensor. The input data type should be float32, float64, int32, int64.
cond(Variable, optional): If is :attr:`None`, the op will create a variable as output tensor, the shape and data type of this \
tensor is the same as input :attr:`x`. If is not :attr:`None`, the op will set the variable as output tensor, the shape and data \
type of this tensor should be the same as input :attr:`x`. Default value is :attr:`None`.
Returns:
Variable, the output data type is bool.: The tensor variable storing the output, the output shape is the same as input :attr:`x`.
Examples:
.. code-block:: python
import paddle.fluid as fluid
label = fluid.layers.data(name='label', shape=[1], dtype='int64')
limit = fluid.layers.fill_constant(shape=[1], value=1, dtype='int64')
out = fluid.layers.not_equal(x=label, y=limit)
"""
helper = LayerHelper("not_equal", **locals())
if cond is None:
cond = helper.create_variable_for_type_inference(dtype='bool')
cond.stop_gradient = True
helper.append_op(
type='not_equal', inputs={'X': [x],
'Y': [y]}, outputs={'Out': [cond]})
return cond
def array_read(array, i):
"""
This OP is used to read data at the specified position from the input array
:ref:`api_fluid_LoDTensorArray` . ``array`` is the input array and ``i``
is the specified read position. This OP is often used together with
:ref:`api_fluid_layers_array_write` OP.
Case 1:
::
Input:
The shape of first three tensors are [1], and that of the last one is [1,2]:
array = ([0.6], [0.1], [0.3], [0.4, 0.2])
And:
i = [3]
Output:
output = [0.4, 0.2]
Args:
array (LoDTensorArray): The input LoDTensorArray.
i (Variable): 1-D Tensor, whose shape is [1] and dtype is int64. It represents the
specified read position of ``array``.
Returns:
Variable: The LoDTensor or Tensor that is read at the specified position of ``array``.
Examples:
.. code-block:: python
# First we're going to create a LoDTensorArray, then we're going to write the Tensor into
# the specified position, and finally we're going to read the Tensor at that position.
import paddle.fluid as fluid
arr = fluid.layers.create_array(dtype='float32')
tmp = fluid.layers.fill_constant(shape=[3, 2], dtype='int64', value=5)
i = fluid.layers.fill_constant(shape=[1], dtype='int64', value=10)
# tmp is the Tensor with shape [3,2], and if we write it into the position with subscript 10
# of the empty-array: arr, then the length of arr becomes 11.
arr = fluid.layers.array_write(tmp, i, array=arr)
# Read the data of the position with subscript 10.
item = fluid.layers.array_read(arr, i)
# You can print out the data via executor.
input = fluid.layers.Print(item, message="The LoDTensor of the i-th position:")
main_program = fluid.default_main_program()
exe = fluid.Executor(fluid.CPUPlace())
exe.run(main_program)
# The printed result is:
# 1569588169 The LoDTensor of the i-th position: The place is:CPUPlace
# Tensor[array_read_0.tmp_0]
# shape: [3,2,]
# dtype: l
# data: 5,5,5,5,5,5,
# the output is 2-D Tensor with shape [3,2].
# dtype is the corresponding C++ data type, which may vary in different environments.
# Eg: if the data type of tensor is int64, then the corresponding C++ data type is int64_t,
# so the dtype value is typeid(int64_t).Name(), which is 'x' on MacOS, 'l' on Linux,
# and '__int64' on Windows. They both represent 64-bit integer variables.
"""
helper = LayerHelper('array_read', **locals())
if not isinstance(
array,
Variable) or array.type != core.VarDesc.VarType.LOD_TENSOR_ARRAY:
raise TypeError("array should be tensor array vairable")
out = helper.create_variable_for_type_inference(dtype=array.dtype)
helper.append_op(
type='read_from_array',
inputs={'X': [array],
'I': [i]},
outputs={'Out': [out]})
return out
def shrink_memory(x, i, table):
"""
This function creates an operator to shrink rnn memory using the RankTable
as mentioned in the input parameter.
NOTE: This API is very low-level API. It is used by DynamicRNN only.
Since the Dynamic RNN uses no-padding way to implement RNN. The sequence
will be sorted by order, and the length of valid memory will be shrink after
each time step.
Args:
x(Variable): The memory object in the previous time step.
i(Variable): The step count variable. A int scalar as LoDTensor.
table(Variable): The RNNRankTable object.
Returns:
the memory variable after shrink.
Examples:
Since this API is very low level API. The example is not provided.
Please reference the implementation of class DynamicRNN for detail
usage.
"""
helper = LayerHelper('shrink_memory', **locals())
out = helper.create_variable_for_type_inference(dtype=x.dtype)
helper.append_op(
type='shrink_rnn_memory',
inputs={'X': [x],
'I': [i],
'RankTable': [table]},
outputs={'Out': [out]},
attrs={})
return out
def array_length(array):
"""
This OP is used to get the length of the input array :ref:`api_fluid_LoDTensorArray` .
It can be used together with :ref:`api_fluid_layers_array_read` , :ref:`api_fluid_layers_array_write` ,
:ref:`api_fluid_layers_While` OP to traverse, read and wirte LoDTensorArray.
Args:
array (LoDTensorArray): The input array that will be used to compute the length.
Returns:
Variable: 1-D Tensor with shape [1], which is the length of array. Datatype: int64.
Examples:
.. code-block:: python
import paddle.fluid as fluid
tmp = fluid.layers.zeros(shape=[10], dtype='int32')
i = fluid.layers.fill_constant(shape=[1], dtype='int64', value=10)
# tmp is 1-D Tensor with shape [10]. We write tmp into arr on subscript 10,
# then the length of arr becomes 11.
arr = fluid.layers.array_write(tmp, i=i)
# return the length of arr
arr_len = fluid.layers.array_length(arr)
# You can use executor to print out the length of LoDTensorArray.
input = fluid.layers.Print(arr_len, message="The length of LoDTensorArray:")
main_program = fluid.default_main_program()
exe = fluid.Executor(fluid.CPUPlace())
exe.run(main_program)
# The printed result is:
# 1569576542 The length of LoDTensorArray: The place is:CPUPlace
# Tensor[array_length_0.tmp_0]
# shape: [1,]
# dtype: l
# data: 11,
# 1-D Tensor with shape [1], whose value is 11. It means that the length of LoDTensorArray
# is 11.
# dtype is the corresponding C++ data type, which may vary in different environments.
# Eg: if the data type of tensor is int64, then the corresponding C++ data type is int64_t,
# so the dtype value is typeid(int64_t).Name(), which is 'x' on MacOS, 'l' on Linux,
# and '__int64' on Windows. They both represent 64-bit integer variables.
"""
helper = LayerHelper('array_length', **locals())
tmp = helper.create_variable_for_type_inference(dtype='int64')
tmp.stop_gradient = True
helper.append_op(
type='lod_array_length', inputs={'X': [array]}, outputs={'Out': [tmp]})
return tmp
class ConditionalBlockGuard(BlockGuard):
"""
ConditionalBlockGuard is derived from BlockGuard. It is dedicated for
holding a ConditionalBlock, and helping users entering and exiting the
ConditionalBlock via Python's 'with' keyword. However, ConditionalBlockGuard
is generally an internal component of IfElse, users should not use it directly.
"""
def __init__(self, block):
if not isinstance(block, ConditionalBlock):
raise TypeError("block should be conditional block")
super(ConditionalBlockGuard, self).__init__(block.helper.main_program)
self.block = block
def __enter__(self):
return super(ConditionalBlockGuard, self).__enter__()
def __exit__(self, exc_type, exc_val, exc_tb):
self.block.complete()
return super(ConditionalBlockGuard, self).__exit__(exc_type, exc_val,
exc_tb)
class ConditionalBlock(object):
'''
**ConditionalBlock**
ConditionalBlock is an operator that bind a block to a specific condition,
if the condition matches, the corresponding block will be executed.
Args:
inputs (Variable): bool conditions.
is_scalar_condition (bool): whether the branch is controled by a scalar.
name(str): name of this ConditionalBlock.
Examples:
.. code-block:: python
import paddle.fluid as fluid
cond = layers.less_than(x=label, y=limit)
true_image, false_image = layers.split_lod_tensor(
input=image, mask=cond)
true_cond = layers.ConditionalBlock([true_image])
with true_cond.block():
...
with false_cond.block():
...
'''
def __init__(self, inputs, is_scalar_condition=False, name=None):
for each_input in inputs:
if not isinstance(each_input, Variable):
raise TypeError("Each input should be variable")
self.inputs = inputs
self.is_scalar_condition = is_scalar_condition
self.helper = LayerHelper('conditional_block', name=name)
def block(self):
return ConditionalBlockGuard(self)
def complete(self):
inside_block = self.helper.main_program.current_block()
parent_block = self.helper.main_program.block(inside_block.parent_idx)
intermediate = set()
params = set()
for each_op in inside_block.ops:
assert isinstance(each_op, Operator)
for iname in each_op.input_names:
for in_var_name in each_op.input(iname):
if in_var_name not in intermediate:
params.add(in_var_name)
for oname in each_op.output_names:
for out_var_name in each_op.output(oname):
intermediate.add(out_var_name)
input_set = set([ipt.name for ipt in self.inputs])
param_list = [
parent_block._var_recursive(each_name) for each_name in params
if each_name not in input_set
]
out_list = []
for inner_out_name in intermediate:
inner_var = parent_block._find_var_recursive(inner_out_name)
if inner_var:
out_list.append(inner_var)
step_scope = parent_block.create_var(
type=core.VarDesc.VarType.STEP_SCOPES)
parent_block.append_op(
type='conditional_block',
inputs={
'Cond': self.inputs,
'Input': param_list,
},
outputs={'Out': out_list,
'Scope': [step_scope]},
attrs={
'sub_block': inside_block,
'is_scalar_condition': self.is_scalar_condition
})
class Switch(object):
"""
This class is used to implement Switch branch control function.
Switch branch contains several case branches and one default branch.
Switch control flow checks whether the case branch conditions are satisfied in turn,
and only executes the statement after the first case branch that satisfies the conditions.
If there is no case branch that satisfies the condition,
only the statement following the default branch is executed.
Member Functions:
case(cond): The case branch of Switch whose parameter cond is a scalar Variable of bool type. Only if the cond of the current case branch is True and the cond of the previous case branch is False, the statement after the case branch will be executed, and the statement after the case branch will not be executed.
default(): The default branch of Switch. When cond of all case branches is False, the statement after default branch is executed.
Case and default functions can only be used inside the scope of Switch, as shown below:
.. code-block:: python
'''
with fluid.layers.Switch() as switch:
with switch.case(cond1):
i = fluid.layers.fill_constant(shape=[1], dtype='int64', value=1)
with switch.case(cond2):
i = fluid.layers.fill_constant(shape=[1], dtype='int64', value=2)
with switch.default():
i = fluid.layers.fill_constant(shape=[1], dtype='int64', value=0)
'''
Args:
name(str, optional): The default value is None. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name` .
Examples:
.. code-block:: python
import paddle.fluid as fluid
lr = fluid.layers.create_global_var(
shape=[1],
value=0.0,
dtype='float32',
persistable=True,
name="learning_rate")
zero_var = fluid.layers.fill_constant(
shape=[1], dtype='float32', value=0.0)
one_var = fluid.layers.fill_constant(
shape=[1], dtype='float32', value=1.0)
two_var = fluid.layers.fill_constant(
shape=[1], dtype='float32', value=2.0)
global_step = fluid.layers.autoincreased_step_counter(counter_name='@LR_DECAY_COUNTER@', begin=0, step=1)
with fluid.layers.control_flow.Switch() as switch:
with switch.case(global_step == zero_var):
fluid.layers.assign(input=one_var, output=lr)
with switch.default():
fluid.layers.assign(input=two_var, output=lr)
exe = fluid.Executor(fluid.CPUPlace())
exe.run(fluid.default_startup_program())
res = exe.run(fluid.default_main_program(), feed={}, fetch_list=[lr])
print(res) # [array([1.], dtype=float32)]
"""
def __init__(self, name=None):
self.helper = LayerHelper('switch', name=name)
self.inside_scope = False
self.pre_not_conditions = []
def case(self, condition):
if not self.inside_scope:
raise ValueError("case should be called inside with")
if len(self.pre_not_conditions) == 0:
cond_block = ConditionalBlock([condition], is_scalar_condition=True)
not_cond = logical_not(x=condition)
self.pre_not_conditions.append(not_cond)
else:
pre_cond_num = len(self.pre_not_conditions)
pre_not_cond = self.pre_not_conditions[pre_cond_num - 1]
new_not_cond = logical_and(
x=pre_not_cond, y=logical_not(x=condition))
self.pre_not_conditions.append(new_not_cond)
cond_block = ConditionalBlock(
[logical_and(
x=pre_not_cond, y=condition)],
is_scalar_condition=True)
return ConditionalBlockGuard(cond_block)
def default(self):
pre_cond_num = len(self.pre_not_conditions)
if pre_cond_num == 0:
raise ValueError("there should be at least one condition")
cond_block = ConditionalBlock(
[self.pre_not_conditions[pre_cond_num - 1]],
is_scalar_condition=True)
return ConditionalBlockGuard(cond_block)
def __enter__(self):
"""
set flag that now is inside switch.block {}
:return:
"""
self.inside_scope = True
return self
def __exit__(self, exc_type, exc_val, exc_tb):
self.inside_scope = False
if exc_type is not None:
return False # re-raise exception
return True
class IfElseBlockGuard(object):
def __init__(self, is_true, ifelse):
if not isinstance(ifelse, IfElse):
raise TypeError("ifelse must be an instance of IfElse class")
if ifelse.status != IfElse.OUT_IF_ELSE_BLOCKS:
raise ValueError("You cannot invoke IfElse.block() inside a block")
self.is_true = is_true
self.ie = ifelse
if is_true:
self.cond_block = ifelse.conditional_true_block
else:
self.cond_block = ifelse.conditional_false_block
if not isinstance(self.cond_block, ConditionalBlock):
raise TypeError("Unexpected situation")
self.cond_block = self.cond_block.block()
def __enter__(self):
self.ie.status = IfElse.IN_IF_ELSE_TRUE_BLOCKS if self.is_true else IfElse.IN_IF_ELSE_FALSE_BLOCKS
self.cond_block.__enter__()
def __exit__(self, exc_type, exc_val, exc_tb):
if not self.cond_block.__exit__(exc_type, exc_val, exc_tb):
# re-raise inside exception
return False
if len(self.ie.output_table[1 if self.is_true else 0]) == 0:
raise ValueError("Must set output inside block")
self.ie.status = IfElse.OUT_IF_ELSE_BLOCKS
class IfElse(object):
"""
This class is used to implement IfElse branch control function. IfElse contains two blocks, true_block and false_block. IfElse will put data satisfying True or False conditions into different blocks to run.
Cond is a 2-D Tensor with shape [N, 1] and data type bool, representing the execution conditions of the corresponding part of the input data.
IfElse OP is different from other OPs in usage, which may cause some users confusion. Here is a simple example to illustrate this OP.
.. code-block:: python
# The following code completes the function: subtract 10 from the data greater than 0 in x, add 10 to the data less than 0 in x, and sum all the data.
import numpy as np
import paddle.fluid as fluid
x = fluid.layers.data(name='x', shape=[4, 1], dtype='float32', append_batch_size=False)
y = fluid.layers.data(name='y', shape=[4, 1], dtype='float32', append_batch_size=False)
x_d = np.array([[3], [1], [-2], [-3]]).astype(np.float32)
y_d = np.zeros((4, 1)).astype(np.float32)
# Compare the size of x, y pairs of elements, output cond, cond is shape [4, 1], data type bool 2-D tensor.
# Based on the input data x_d, y_d, it can be inferred that the data in cond are [[true], [true], [false], [false]].
cond = fluid.layers.greater_than(x, y)
# Unlike other common OPs, ie below returned by the OP is an IfElse OP object
ie = fluid.layers.IfElse(cond)
with ie.true_block():
# In this block, according to cond condition, the data corresponding to true dimension in X is obtained and subtracted by 10.
out_1 = ie.input(x)
out_1 = out_1 - 10
ie.output(out_1)
with ie.false_block():
# In this block, according to cond condition, get the data of the corresponding condition in X as false dimension, and add 10
out_1 = ie.input(x)
out_1 = out_1 + 10
ie.output(out_1)
# According to cond condition, the data processed in the two blocks are merged. The output here is output, the type is List, and the element type in List is Variable.
output = ie() # [array([[-7.], [-9.], [ 8.], [ 7.]], dtype=float32)]
# Get the first Variable in the output List and add all elements.
out = fluid.layers.reduce_sum(output[0])
exe = fluid.Executor(fluid.CPUPlace())
exe.run(fluid.default_startup_program())
res = exe.run(fluid.default_main_program(), feed={"x":x_d, "y":y_d}, fetch_list=[out])
print res
# [array([-1.], dtype=float32)]
Args:
cond (Variable): cond is a 2-D Tensor with shape [N, 1] and data type bool, representing the corresponding execution conditions of N input data. The data type is bool.
name(str, optional): The default value is None. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name` .
Returns:
Unlike other common OPs, the OP call returns an IfElse OP object (e.g. ie in the example), which branches the input data by calling the internal functions of the object ``true_block ()``, ``false_block ()``, ``input ()``, ``output ()``, and integrates the data processed by different branches as the overall output by calling the internal ``call ()`` function. The output type is a list, and the type of each element in the list is Variable.
Internal Functions:
The block is constructed by calling the ``with ie. true_block()`` function in the object, and the computational logic under condition true is put into the block. If no corresponding block is constructed, the input data in the corresponding conditional dimension is unchanged.
The block is constructed by calling the ``with ie. false_block()`` function in the object, and the computational logic under condition false is put into the block. If no corresponding block is constructed, the input data in the corresponding conditional dimension is unchanged.
``Out = ie. input (x)`` will take out the data of the corresponding conditional dimension in X and put it into out, supporting the internal processing of multiple inputs in block.
``ie. output (out)`` writes the result to the output of the corresponding condition.
There is a ``call ()`` function inside the object, that is, by calling ``output = ie ()``, all the outputs inside the block of False are fused as the whole output, the output type is a list, and the type of each element in the list is Variable.
"""
OUT_IF_ELSE_BLOCKS = 0
IN_IF_ELSE_TRUE_BLOCKS = 1
IN_IF_ELSE_FALSE_BLOCKS = 2
def __init__(self, cond, name=None):
if not isinstance(cond, Variable):
raise TypeError("cond must be a Variable")
self.helper = LayerHelper('ifelse', name=name)
self.cond = cond
self.input_table = {}
self.status = IfElse.OUT_IF_ELSE_BLOCKS
self.conditional_true_block = ConditionalBlock(inputs=[self.cond])
self.conditional_false_block = ConditionalBlock(inputs=[self.cond])
self.output_table = ([], []) # (true_outs, false_outs)
def input(self, x):
if self.status == IfElse.OUT_IF_ELSE_BLOCKS:
raise ValueError("input must in true/false blocks")
if id(x) not in self.input_table:
parent_block = self._parent_block()
out_true = parent_block.create_var(
name=unique_name.generate_with_ignorable_key('ifelse_input' +
self.helper.name),
dtype=x.dtype)
out_false = parent_block.create_var(
name=unique_name.generate_with_ignorable_key('ifelse_input' +
self.helper.name),
dtype=x.dtype)
parent_block.append_op(
type='split_lod_tensor',
inputs={
'X': x,
'Mask': self.cond,
},
outputs={'OutTrue': out_true,
'OutFalse': out_false},
attrs={'level': 0})
self.input_table[id(x)] = (out_true, out_false)
else:
out_true, out_false = self.input_table[id(x)]
if self.status == IfElse.IN_IF_ELSE_TRUE_BLOCKS:
return out_true
else:
return out_false
def _parent_block(self):
current_block = self.helper.main_program.current_block()
return self.helper.main_program.block(current_block.parent_idx)
def true_block(self):
return IfElseBlockGuard(True, self)
def false_block(self):
return IfElseBlockGuard(False, self)
def output(self, *outs):
if self.status == self.OUT_IF_ELSE_BLOCKS:
raise ValueError("output can only be invoked in the sub-block")
out_table = self.output_table[1 if self.status ==
self.IN_IF_ELSE_TRUE_BLOCKS else 0]
parent_block = self._parent_block()
for each_out in outs:
if not isinstance(each_out, Variable):
raise TypeError("Each output should be a variable")
# create outside tensor
outside_out = parent_block.create_var(
name=unique_name.generate_with_ignorable_key("_".join(
[self.helper.name, 'output'])),
dtype=each_out.dtype)
out_table.append(outside_out)
# assign local var to outside
assign(input=each_out, output=outside_out)
def __call__(self):
if self.status != self.OUT_IF_ELSE_BLOCKS:
raise ValueError("IfElse::__call__ must be out of sub-block")
false_len, true_len = list(map(len, self.output_table))
if false_len == 0 and true_len == 0:
raise ValueError("Must invoke true_block/false_block before "
"__call__")
elif false_len != true_len and false_len != 0 and true_len != 0:
raise ValueError("The output side must be same")
elif false_len == 0 or true_len == 0:
return self.output_table[0 if false_len != 0 else 1]
# else none of false_len/true_len is zero
# merge together
rlist = []
for false_var, true_var in zip(*self.output_table):
rlist.append(
merge_lod_tensor(
in_true=true_var,
in_false=false_var,
mask=self.cond,
x=self.cond,
level=0))
return rlist
class DynamicRNN(object):
"""
**Note: the input of this class should be LoDTensor which holds the
information of variable-length sequences. If the input is fixed-length Tensor,
please use StaticRNN (fluid.layers.** :ref:`api_fluid_layers_StaticRNN` **) for
better performance.**
DynamicRNN can process a minibatch of variable-length sequences.
The length of each sample can be different and is recorded in LoD.
In DynamicRNN, an input sequence will be unfolded into time steps and users
can define how to process each time step in :code:`block()` .
The total number of time steps is determined by the longest sequence.
DynamicRNN will not pad all sequences to the same length, instead it will
sort the sequences internally by the sequence length in descending order.
The input sequences will be shrinked because only sequences of which the
length is larger than the time step will participate the remaining calculation.
If defined :code:`drnn = DynamicRNN()`, then users can call :code:`drnn()`
to obtain the result sequences. It is a LoDTensor gained by merging all
time steps's output. When RNN's input sequence x meets :code:`x.lod_level == 1`,
the output LoDTensor will have the same LoD with x. The result of :code:`drnn()`
includes RNN's outputs of all time steps, users can call
:ref:`api_fluid_layers_sequence_last_step` to extract the data of the last time step.
Warning:
Currently it is not supported to set :code:`is_sparse = True` of any
layers defined within DynamicRNN's :code:`block` function.
Args:
name (str, optional): The default value is None. Normally there is no
need for user to set this property. For more information,
please refer to :ref:`api_guide_Name` .
Examples:
.. code-block:: python
import paddle.fluid as fluid
sentence = fluid.data(name='sentence', shape=[None, 32], dtype='float32', lod_level=1)
encoder_proj = fluid.data(name='encoder_proj', shape=[None, 32], dtype='float32', lod_level=1)
decoder_boot = fluid.data(name='boot', shape=[None, 10], dtype='float32')
drnn = fluid.layers.DynamicRNN()
with drnn.block():
# Set sentence as RNN's input, each time step processes a word from the sentence
current_word = drnn.step_input(sentence)
# Set encode_proj as RNN's static input
encoder_word = drnn.static_input(encoder_proj)
# Initialize memory with boot_memory, which need reorder according to RNN's input sequences
memory = drnn.memory(init=decoder_boot, need_reorder=True)
fc_1 = fluid.layers.fc(input=encoder_word, size=30)
fc_2 = fluid.layers.fc(input=current_word, size=30)
decoder_inputs = fc_1 + fc_2
hidden, _, _ = fluid.layers.gru_unit(input=decoder_inputs, hidden=memory, size=30)
# Update memory with hidden
drnn.update_memory(ex_mem=memory, new_mem=hidden)
out = fluid.layers.fc(input=hidden, size=10, bias_attr=True, act='softmax')
# Set hidden and out as RNN's outputs
drnn.output(hidden, out)
# Get RNN's result
hidden, out = drnn()
# Get RNN's result of the last time step
last = fluid.layers.sequence_last_step(out)
"""
BEFORE_RNN = 0
IN_RNN = 1
AFTER_RNN = 2
def __init__(self, name=None):
self.helper = LayerHelper('dynamic_rnn', name=name)
self.status = DynamicRNN.BEFORE_RNN
self.lod_rank_table = None
self.max_seq_len = None
self.step_idx = None
self.zero_idx = None
self.mem_dict = dict()
self.output_array = []
self.outputs = []
self.cond = self.helper.create_variable_for_type_inference(dtype='bool')
self.cond.stop_gradient = False
self.while_op = While(self.cond)
self.input_array = []
self.mem_link = []
def step_input(self, x, level=0):
"""
This function is used to set sequence x as DynamicRNN's input.
The maximum sequence length in x determines the number of time steps
the RNN unit will be executed. DynamicRNN can take multiple inputs.
When all inputs' :code:`lod_level` are 1, all inputs should hold the
same LoD. When :code:`x.lod_level >= 2` , the input sequence will be
unfold along specified level, and the slice of each time step is a
LoDTensor whose lod_level is :code:`x.lod_level - level - 1` .
In this case, the specified LoD level of multiple inputs should be the same.
- Case 1:
.. code-block:: text
# input, where Si is slice data of shape [1, N]
level = 0
x.lod = [[2, 1, 3]]
x.shape = [6, N]
x.data = [[S0],
[S0],
[S1],
[S2],
[S2],
[S2]]
# output
# step 0, time step data of 3 sequences
out.lod = [[]]
out.shape = [3, N]
out.data = [[S2],
[S0],
[S1]]
# step 1, time step data of 2 sequences
out.lod = [[]]
out.shape = [2, N]
out.data = [[S2],
[S0]]
# step 2, time step data of 1 sequences
out.lod = [[]]
out.shape = [1, N]
out.data = [[S2]]
Args:
x (Variable): The input LoDTensor which holds information of a
minibatch of variable-length sequences and should meet :code:`x.lod_level >= 1` .
When RNN has multiple inputs, the first dimension should match
across all inputs, but other shape components may differ.
Optional data types are: bool, float16, float32, float64, int8, int16, int32, int64, uint8.
level (int, optional): The level of lod used to split steps.
It should be in range :math:`[0, x.lod\_level)` . The default value is 0.
Returns:
Variable: The current time step in the input sequence. If there are :code:`num_sequences` \
sequences in x whose length is larger than :code:`step_idx` , the returned Variable \
will only hold the :code:`step_idx` -th time step of those `num_sequences` sequences. \
The data type is the same as input. If :code:`x.lod_level == 1` , the return value is \
a Tensor of shape :math:`\{num\_sequences, x.shape[1], ...\}` , or it will \
be a variable-length LoDTensor.
Raises:
ValueError: When :code:`step_input()` is called outside :code:`block()` .
TypeError: When x is not a Variable.
Examples:
.. code-block:: python
import paddle.fluid as fluid
sentence = fluid.data(name='sentence', shape=[None, 1], dtype='int64', lod_level=1)
embedding = fluid.layers.embedding(input=sentence, size=[65536, 32], is_sparse=True)
drnn = fluid.layers.DynamicRNN()
with drnn.block():
# Set embedding as RNN's input, each time step processes a word from the sentence
word = drnn.step_input(embedding)
# Initialize memory to a Tensor whose value is 0, shape=[batch_size, 200],
# where batch_size is the number of sequences in embedding.
memory = drnn.memory(shape=[200])
hidden = fluid.layers.fc(input=[word, memory], size=200, act='relu')
# Update memory to hidden
drnn.update_memory(ex_mem=memory, new_mem=hidden)
# Set hidden as RNN's output
drnn.output(hidden)
# Get RNN's result
rnn_output = drnn()
"""
self._assert_in_rnn_block_("step_input")
if not isinstance(x, Variable):
raise TypeError(
"step_input() can only take a Variable as its input.")
parent_block = self._parent_block_()
if self.lod_rank_table is None:
self.lod_rank_table = parent_block.create_var(
name=unique_name.generate('lod_rank_table'),
type=core.VarDesc.VarType.LOD_RANK_TABLE)
self.lod_rank_table.stop_gradient = True
parent_block.append_op(
type='lod_rank_table',
inputs={"X": x},
outputs={"Out": self.lod_rank_table},
attrs={"level": level})
self.max_seq_len = parent_block.create_var(
name=unique_name.generate('dynamic_rnn_max_seq_len'),
dtype='int64')
self.max_seq_len.stop_gradient = False
parent_block.append_op(
type='max_sequence_len',
inputs={'RankTable': self.lod_rank_table},
outputs={"Out": self.max_seq_len})
self.cond.stop_gradient = True
parent_block.append_op(
type='less_than',
inputs={'X': self.step_idx,
'Y': self.max_seq_len},
outputs={'Out': self.cond},
attrs={'force_cpu': True})
input_array = parent_block.create_var(
name=unique_name.generate('dynamic_rnn_input_array'),
type=core.VarDesc.VarType.LOD_TENSOR_ARRAY,
dtype=x.dtype)
self.input_array.append((input_array, x.dtype))
parent_block.append_op(
type='lod_tensor_to_array',
inputs={'X': x,
'RankTable': self.lod_rank_table},
outputs={'Out': input_array})
return array_read(array=input_array, i=self.step_idx)
def static_input(self, x):
"""
This function is used to set x as DynamicRNN's static input. It is optional.
- Case 1, set static input with LoD
.. code-block:: text
# RNN's input is the same as the case listed in step_input
# static input, where Si is slice data of shape [1, M]
x.lod = [[3, 1, 2]]
x.shape = [6, M]
x.data = [[S0],
[S0],
[S0],
[S1],
[S2],
[S2]]
# step 0, batch data corresponding to the 3 input sequences
out.lod = [[2, 3, 1]]
out.shape = [6, M]
out.data = [[S2],
[S2],
[S0],
[S0],
[S0],
[S1]]
# step 1, batch data corresponding to the 2 input sequences
out.lod = [[2, 3]]
out.shape = [5, M]
out.data = [[S2],
[S2],
[S0],
[S0],
[S0]]
# step 2, batch data corresponding to the 1 input sequences
out.lod = [[2]]
out.shape = [2, M]
out.data = [[S2],
[S2]]
- Case 2, set static input without LoD
.. code-block:: text
# RNN's input is the same as the case listed in step_input
# static input, where Si is slice data of shape [1, M]
x.lod = [[]]
x.shape = [3, M]
x.data = [[S0],
[S1],
[S2]]
# step 0, batch data corresponding to the 3 input sequences
out.lod = [[]]
out.shape = [3, M]
out.data = [[S2],
[S0],
[S1]]
# step 1, batch data corresponding to the 2 input sequences
out.lod = [[]]
out.shape = [2, M]
out.data = [[S2],
[S0]]
# step 2, batch data corresponding to the 1 input sequences
out.lod = [[]]
out.shape = [1, M]
out.data = [[S2]]
Args:
x (Variable): The static input LoDTensor which should hold the same number of sequences
as RNN's input (the input LoDTensor set by :code:`step_input()` ). If the LoD is None,
the input x will be treated as a minibatch with :code:`x.shape[0]` sequences of length 1.
Optional data types are: bool, float16, float32, float64, int8, int16, int32, int64, uint8.
Returns:
Variable: The input LoDTensor after sorted and shrinked. If there are :code:`num_sequences` \
sequences in RNN's input LoDTensor whose length is larger than :code:`step_idx` , \
the static input Tensor will be sorted to the same order as RNN's input and \
will only retain data corresponding to those :code:`num_sequences` sequences. \
The data type is the same as input. If :code:`x.lod == None` , the return value is \
a Tensor of shape :math:`\{num\_sequences, x.shape[1], ...\}` , or it will \
be a variable-length LoDTensor.
Raises:
ValueError: When :code:`static_input()` is called outside :code:`block()` .
TypeError: When x is not a Variable.
RuntimeError: When :code:`static_input()` is called before :code:`step_input()` .
Examples:
.. code-block:: python
import paddle.fluid as fluid
sentence = fluid.data(name='sentence', shape=[None, 32], dtype='float32', lod_level=1)
encoder_proj = fluid.data(name='encoder_proj', shape=[None, 32], dtype='float32', lod_level=1)
decoder_boot = fluid.data(name='boot', shape=[None, 10], dtype='float32')
drnn = fluid.layers.DynamicRNN()
with drnn.block():
# Set sentence as RNN's input, each time step processes a word from the sentence
current_word = drnn.step_input(sentence)
# Set encode_proj as RNN's static input
encoder_word = drnn.static_input(encoder_proj)
# Initialize memory with boot_memory, which need reorder according to RNN's input sequences
memory = drnn.memory(init=decoder_boot, need_reorder=True)
fc_1 = fluid.layers.fc(input=encoder_word, size=30)
fc_2 = fluid.layers.fc(input=current_word, size=30)
decoder_inputs = fc_1 + fc_2
hidden, _, _ = fluid.layers.gru_unit(input=decoder_inputs, hidden=memory, size=30)
# Update memory with hidden
drnn.update_memory(ex_mem=memory, new_mem=hidden)
out = fluid.layers.fc(input=hidden, size=10, bias_attr=True, act='softmax')
# Set out as RNN's output
drnn.output(out)
# Get RNN's result
rnn_output = drnn()
"""
self._assert_in_rnn_block_("static_input")
if not isinstance(x, Variable):
raise TypeError(
"static_input() can only take a Variable as its input")
if self.lod_rank_table is None:
raise RuntimeError(
"static_input() must be called after step_input().")
parent_block = self._parent_block_()
x_reordered = parent_block.create_var(
name=unique_name.generate("dynamic_rnn_static_input_reordered"),
type=core.VarDesc.VarType.LOD_TENSOR,
dtype=x.dtype)
parent_block.append_op(
type='reorder_lod_tensor_by_rank',
inputs={'X': [x],
'RankTable': [self.lod_rank_table]},
outputs={'Out': [x_reordered]})
return shrink_memory(x_reordered, self.step_idx, self.lod_rank_table)
@signature_safe_contextmanager
def block(self):
"""
The function is used to list the operations executed during
each time step in RNN. The operation list will be executed :code:`max_sequence_len`
times (where :code:`max_sequence_len` is the maximum length of RNN's input sequences).
Raises:
ValueError: When :code:`block()` is called multi-times.
"""
if self.status != DynamicRNN.BEFORE_RNN:
raise ValueError("rnn.block() can only be invoke once")
self.step_idx = fill_constant(
shape=[1], dtype='int64', value=0, force_cpu=True)
self.step_idx.stop_gradient = False
self.status = DynamicRNN.IN_RNN
with self.while_op.block():
yield
increment(x=self.step_idx, value=1.0, in_place=True)
for new_mem, mem_array in self.mem_link:
array_write(x=new_mem, i=self.step_idx, array=mem_array)
less_than(
x=self.step_idx,
y=self.max_seq_len,
force_cpu=True,
cond=self.cond)
self.status = DynamicRNN.AFTER_RNN
for each_array in self.output_array:
self.outputs.append(
array_to_lod_tensor(
x=each_array, table=self.lod_rank_table))
def __call__(self, *args, **kwargs):
"""
This function is used to get the output sequneces of DynamicRNN.
Args:
None
Returns:
Variable or Variable list: RNN's output sequences.
Raises:
ValueError: When :code:`__call__()` is called before :code:`block()` .
"""
if self.status != DynamicRNN.AFTER_RNN:
raise ValueError(("Output of the dynamic RNN can only be visited "
"outside the rnn block."))
if len(self.outputs) == 1:
return self.outputs[0]
else:
return self.outputs
def memory(self,
init=None,
shape=None,
value=0.0,
need_reorder=False,
dtype='float32'):
"""
Create a memory Variable for DynamicRNN to deliver data cross time steps.
It can be initialized by an existing Tensor or a constant Tensor of given
dtype and shape.
Args:
init (Variable, optional): LoDTensor used to initialize the memory.
If init is not None, it should hold the same number of sequences
as RNN's input (the input LoDTensor set by :code:`step_input()` )
and the memory will be initialized to it. If init's LoD is None,
it will be treated as a minibatch with :code:`init.shape[0]` sequences
of length 1. The default value is None.
shape (list|tuple, optional): When init is None, it is used to specify
the memory's shape. Note that the shape does not include the batch_size.
If setting shape to :math:`\{D_1, D_2, ...\}` , the shape of memory Tensor
will be :math:`\{batch\_size, D_1, D_2, ...\}` , where batch_size is
determined by RNN's input sequences. The default value is None.
value (float, optional): When init is None, it is used as initalized value
of memory. The default value is 0.0.
need_reorder (bool, optional): When init is not None, it determines whether
the memory needs to reorder like the RNN's input sequeneces. It should be
set to True when the initialized memory depends on the order of input samples.
The default value is False.
dtype (str|numpy.dtype, optional): When init is None, it is used to set the
data type of memory. The default value is "float32". Optional data types
are: "float32", "float64", "int32", "int64".
Returns:
Variable: The memory LoDTensor after shrinked. If there are :code:`num_sequences` \
sequences in RNN's input LoDTensor whose length is larger than :code:`step_idx` , \
the memory Tensor also need to be shrinked and will only retain data \
corresponding to those :code:`num_sequences` sequences.
Raises:
ValueError: When :code:`memory()` is called outside :code:`block()` .
TypeError: When init is set and is not a Variable.
ValueError: When :code:`memory()` is called before :code:`step_input()` .
Examples:
.. code-block:: python
import paddle.fluid as fluid
sentence = fluid.data(name='sentence', shape=[None, 32], dtype='float32', lod_level=1)
boot_memory = fluid.data(name='boot', shape=[None, 10], dtype='float32')
drnn = fluid.layers.DynamicRNN()
with drnn.block():
# Set sentence as RNN's input, each time step processes a word from the sentence
word = drnn.step_input(sentence)
# Initialize memory with boot_memory, which need reorder according to RNN's input sequences
memory = drnn.memory(init=boot_memory, need_reorder=True)
hidden = fluid.layers.fc(input=[word, memory], size=10, act='tanh')
# Update memory with hidden
drnn.update_memory(ex_mem=memory, new_mem=hidden)
# Set hidden as RNN's output
drnn.output(hidden)
# Get RNN's result
rnn_output = drnn()
Examples:
.. code-block:: python
import paddle.fluid as fluid
sentence = fluid.data(name='sentence', shape=[None, 32], dtype='float32', lod_level=1)
drnn = fluid.layers.DynamicRNN()
with drnn.block():
# Set sentence as RNN's input, each time step processes a word from the sentence
word = drnn.step_input(sentence)
# Initialize memory to a Tensor whose value is 0, shape=[batch_size, 10],
# where batch_size is the number of sequences in sentence.
memory = drnn.memory(shape=[10], dtype='float32', value=0)
hidden = fluid.layers.fc(input=[word, memory], size=10, act='tanh')
# Update memory with hidden
drnn.update_memory(ex_mem=memory, new_mem=hidden)
# Set hidden as RNN's output
drnn.output(hidden)
# Get RNN's result
rnn_output = drnn()
"""
self._assert_in_rnn_block_('memory')
self._init_zero_idx_()
if init is not None:
if not isinstance(init, Variable):
raise TypeError(
"The input arg `init` of memory() must be a Variable")
parent_block = self._parent_block_()
init_tensor = init
if need_reorder == True:
if self.lod_rank_table is None:
raise ValueError(
'If set need_reorder to True, make sure step_input be '
'invoked before '
'memory(init=init, need_reordered=True, ...).')
init_reordered = parent_block.create_var(
name=unique_name.generate('dynamic_rnn_mem_init_reordered'),
type=core.VarDesc.VarType.LOD_TENSOR,
dtype=init.dtype)
parent_block.append_op(
type='reorder_lod_tensor_by_rank',
inputs={
'X': [init_tensor],
'RankTable': [self.lod_rank_table]
},
outputs={'Out': [init_reordered]})
init_tensor = init_reordered
mem_array = parent_block.create_var(
name=unique_name.generate('dynamic_rnn_mem_array'),
type=core.VarDesc.VarType.LOD_TENSOR_ARRAY,
dtype=init.dtype)
parent_block.append_op(
type='write_to_array',
inputs={'X': init_tensor,
'I': self.zero_idx},
outputs={'Out': mem_array})
retv = array_read(array=mem_array, i=self.step_idx)
retv = shrink_memory(
x=retv, i=self.step_idx, table=self.lod_rank_table)
self.mem_dict[retv.name] = mem_array
return retv
else:
if len(self.input_array) == 0:
raise ValueError(
"step_input should be invoked before memory(shape=..., value=...)"
)
parent_block = self._parent_block_()
init = parent_block.create_var(
name=unique_name.generate('mem_init'), dtype=dtype)
arr, dtype = self.input_array[0]
in0 = parent_block.create_var(
name=unique_name.generate('in0'), dtype=dtype)
parent_block.append_op(
type='read_from_array',
inputs={'X': [arr],
'I': [self.zero_idx]},
outputs={'Out': [in0]})
parent_block.append_op(
type='fill_constant_batch_size_like',
inputs={'Input': [in0]},
outputs={'Out': [init]},
attrs={
'shape': [-1] + shape,
'value': float(value),
'dtype': init.dtype
})
return self.memory(init=init)
def update_memory(self, ex_mem, new_mem):
"""
Update the memory which need to be delivered across time steps.
Args:
ex_mem (Variable): The memory data of previous time step.
new_mem (Variable): The new memory data produced in current time step.
The shape and data type of ex_mem and new_mem should be the same.
Returns:
None
Raises:
ValueError: When :code:`update_memory()` is called outside :code:`block()` .
TypeError: When :code:`ex_mem` or :code:`new_mem` is not a Variable.
ValueError: When :code:`ex_mem` is defined by :code:`memory()` .
ValueError: When :code:`update_memory()` is called before :code:`step_input()` .
"""
self._assert_in_rnn_block_('update_memory')
if not isinstance(ex_mem, Variable):
raise TypeError("The input arg `ex_mem` of update_memory() must "
"be a Variable")
if not isinstance(new_mem, Variable):
raise TypeError("The input arg `new_mem` of update_memory() must "
"be a Variable")
mem_array = self.mem_dict.get(ex_mem.name, None)
if mem_array is None:
raise ValueError("Please invoke memory before update_memory")
if self.lod_rank_table is None:
raise ValueError("Please invoke step_input before update_memory")
self.mem_link.append((new_mem, mem_array))
def output(self, *outputs):
"""
This function is used to set :code:`outputs` as RNN's output.
Args:
*outputs (Variable ...): The output Tensor. DynamicRNN can mark multiple
Variables as its output.
Returns:
None
Raises:
ValueError: When :code:`output()` is called outside :code:`block()` .
"""
self._assert_in_rnn_block_('output')
parent_block = self._parent_block_()
for each in outputs:
outside_array = parent_block.create_var(
name=unique_name.generate_with_ignorable_key("_".join(
[self.helper.name, "output_array", each.name])),
type=core.VarDesc.VarType.LOD_TENSOR_ARRAY,
dtype=each.dtype)
array_write(x=each, i=self.step_idx, array=outside_array)
self.output_array.append(outside_array)
def _init_zero_idx_(self):
if self.zero_idx is None:
parent_block = self._parent_block_()
self.zero_idx = parent_block.create_var(
name=unique_name.generate('zero_idx'), dtype='int64')
parent_block.append_op(
type='fill_constant',
inputs={},
outputs={'Out': [self.zero_idx]},
attrs={
'shape': [1],
'dtype': self.zero_idx.dtype,
'value': float(0),
'force_cpu': True
})
def _parent_block_(self):
prog = self.helper.main_program
parent_idx = prog.current_block().parent_idx
assert parent_idx >= 0
parent_block = prog.block(parent_idx)
return parent_block
def _assert_in_rnn_block_(self, method):
if self.status != DynamicRNN.IN_RNN:
raise ValueError("{0} can only be invoked inside rnn block.".format(
method))
@templatedoc()
def reorder_lod_tensor_by_rank(x, rank_table):
"""
${comment}
Args:
x(${x_type}): ${x_comment}.
rank_table(${rank_table_type}): ${rank_table_comment}.
Returns:
out(${out_type}): ${out_comment}.
Examples:
.. code-block:: python
import paddle.fluid as fluid
data_desc = (['input', [9], 0], ['ref', [5], 1])
data = fluid.layers.data(name=data_desc[0][0], shape=data_desc[0][1])
rank_data = fluid.layers.data(name=data_desc[1][0], shape=data_desc[1][1])
table = fluid.layers.control_flow.lod_rank_table(rank_data)
new_data = fluid.layers.reorder_lod_tensor_by_rank(
x=data, rank_table=table)
"""
helper = LayerHelper('reorder_lod_tensor_by_rank', **locals())
helper.is_instance('x', Variable)
helper.is_instance('rank_table', Variable)
out = helper.create_variable_for_type_inference(dtype=x.dtype)
helper.append_op(
type='reorder_lod_tensor_by_rank',
inputs={'X': [x],
'RankTable': [rank_table]},
outputs={'Out': [out]})
return out
def is_empty(x, cond=None):
"""
Test whether a Variable is empty.
Args:
x (Variable): The Variable to be tested.
cond (Variable, optional): Output parameter. Default: None. If this parameter is given, it
saves the test result of given 'x'.
Returns:
Variable: A bool scalar. True if 'x' is an empty Variable.
Raises:
TypeError: If input cond is not a variable, or cond's dtype is
not bool.
Examples:
.. code-block:: python
import paddle.fluid as fluid
input = fluid.layers.data(name="input", shape=[4, 32, 32], dtype="float32")
res = fluid.layers.is_empty(x=input)
# or:
# fluid.layers.is_empty(x=input, cond=res)
"""
helper = LayerHelper("is_empty", **locals())
if cond is None:
cond = helper.create_variable_for_type_inference(dtype='bool')
cond.stop_gradient = True
elif not isinstance(cond, Variable):
raise TypeError("cond takes a variable")
elif cond.dtype != 'bool':
raise TypeError("The data type of cond must be bool")
helper.append_op(
type='is_empty', inputs={'X': [x]}, outputs={'Out': [cond]})
return cond